WiSARDrp for Change Detection in Video Sequences

Weightless neural networks have been successfully used as learners and detectors of background regions in video processing, as they feature fast learning algorithm, noise tolerance and an incremental update of learnt knowledge, also referred to as online training. These features make weightless neural networks suitable and effective to be used for change (motion) detection in scenarios in which environmental changes (light, camera view, cluttered background) and moving objects force the modeling of background regions to change continuously and in drastic ways. In this paper, we present a change detection method in video processing that uses a weightless neural system, called WiSARDrp, as underlying learning mechanism, equipped with a reinforcing/weakening scheme, that builds and continuously updates a model of background at pixel-level. The performance of the proposed background modeling and change detection techniques are evaluated on the ChangeDetection.net video archive.